SOTAVerified

Quantization

Quantization is a promising technique to reduce the computation cost of neural network training, which can replace high-cost floating-point numbers (e.g., float32) with low-cost fixed-point numbers (e.g., int8/int16).

Source: Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 926–950 of 4925 papers

TitleStatusHype
Mixed-Precision Quantization for Deep Vision Models with Integer Quadratic ProgrammingCode0
MINT: Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural NetworksCode0
Minimal Random Code Learning: Getting Bits Back from Compressed Model ParametersCode0
Mirror Descent View for Neural Network QuantizationCode0
Accurate and Efficient Fine-Tuning of Quantized Large Language Models Through Optimal BalanceCode0
MetaAug: Meta-Data Augmentation for Post-Training QuantizationCode0
Memory-Driven Mixed Low Precision Quantization For Enabling Deep Network Inference On MicrocontrollersCode0
Merge-Friendly Post-Training Quantization for Multi-Target Domain AdaptationCode0
McQueen : Mixed Precision Quantization of Early Exit NetworksCode0
Megapixel Image Generation with Step-Unrolled Denoising AutoencodersCode0
Mitigating Quantization Errors Due to Activation Spikes in GLU-Based LLMsCode0
A Simple Low-bit Quantization Framework for Video Snapshot Compressive ImagingCode0
Model Compression with Adversarial Robustness: A Unified Optimization FrameworkCode0
Make RepVGG Greater Again: A Quantization-aware ApproachCode0
Maestro: Uncovering Low-Rank Structures via Trainable DecompositionCode0
Minimize Quantization Output Error with Bias CompensationCode0
Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and EfficiencyCode0
LVPNet: A Latent-variable-based Prediction-driven End-to-end Framework for Lossless Compression of Medical ImagesCode0
Compressing Word Embeddings via Deep Compositional Code LearningCode0
LSQ++: Lower running time and higher recall in multi-codebook quantizationCode0
LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model CompressionCode0
Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-AirCode0
Compressing Vision Transformers for Low-Resource Visual LearningCode0
LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural NetworksCode0
LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling MatricesCode0
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All datasetsImageNetCIFAR-10Wiki-40BAgeDB-30CFP-FPCOCO (Common Objects in Context)IJB-BIJB-CKnowledge-based:LFW

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03—Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31—Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97—Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71—Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2—Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51—Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17—Unverified
8Xception W8A8Top-1 Accuracy (%)78.97—Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88—Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04—Unverified
2DTQMAP0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3—Unverified
2OutEffHop-Bert_basePerplexity6.21—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92—Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8—Unverified